Abstract
Condition monitoring is a developing discipline in machinery maintenance. Data such as vibration levels (both overall and in terms of frequency spectra), temperature, oil analysis, etc, are acquired from plant, and analyzed to determine the condition of that plant at the time of measurement. Software packages are currently available to allow graphical display of the data, with varying degrees of diagnostic tools available to assist engineers in performing data analysis. Furthermore, some rule-based expert systems are available to perform machinery defect diagnosis; again there are varying degrees of automation and human interaction in these packages. However, these systems only deal successfully with clearly defined problems within a narrow band of parameters; they are notably unsuccessful at coping with contradictory, incomplete, or “noisy” data — just the type of data found in many real-world applications.
This paper describes the implementation of an off-line condition monitoring system at Blyth Power Station, one of the stations owned by National Power in the United Kingdom. It explains the application area and the type of data acquired. The paper then goes on to describe the neural network models which have been developed to analyze condition monitoring data.
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© 1995 Springer-Verlag London Limited
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MacIntyre, J., Smith, P. (1995). Condition Monitoring with National Power. In: Kappen, B., Gielen, S. (eds) Neural Networks: Artificial Intelligence and Industrial Applications. Springer, London. https://doi.org/10.1007/978-1-4471-3087-1_56
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DOI: https://doi.org/10.1007/978-1-4471-3087-1_56
Publisher Name: Springer, London
Print ISBN: 978-3-540-19992-2
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